A Survey on Ecg Signal Denoising Using S-Transform and Sg Filtering

Muthusivagami, R.; Prof, Asst; Electronics, Msrc

doi:10.21474/ijar01/11497

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…If the data window is excessively wide, it may result in the loss of valuable information in the signal. Conversely, if the data window is too narrow to effectively filter the signal, opting for a large polynomial degree might introduce new noise, while selecting a small polynomial degree could cause distortion due to oversmoothing of the signal [14,21]. Given that the AEF signal is non-stationary, fixing both the data window size and the polynomial degree in signal smoothing could lead to the loss of original information in the signal.…”

Section: Eewt-asgmentioning

confidence: 99%

Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho

Li,

Yang,

Yin

et al. 2023

Atmosphere

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The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on canceling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky–Golay filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning events. This method can issue a warning signal up to 22 min in advance for lightning processes.

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Section: Eewt-asgmentioning

confidence: 99%

Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho

Li,

Yang,

Yin

et al. 2023

Atmosphere

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“…The phase of the Fourier transform is very sensitive to noise. Even if a small segment of the data is wrong, the phase obtained by the Fourier transform will be much different from the real phase [29]. But because it still uses a continuous periodic sine function as the transform base function, it leads to its time domain.…”

Section: Ecg Signalmentioning

confidence: 99%

A New Type of ECG Signal Acquisition and Storage Nonvolatile Chip Embedded in Mobile Devices for Sports Monitoring

Zhang

2022

Journal of Nanomaterials

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In the fast-paced development of life in today’s society, the use of ECG signals is not only used in the medical direction to scan patients; portable ECG signal acquisition tools can collect and analyze the corresponding ECG signals in time and respond to the wearer in time, so that the user can make timely adjustments or seek medical treatment in time to avoid a malignant situation that occurs. For people in sports, the collection of ECG signals is more helpful to the arrangement and estimation of training conditions. Based on nonvolatile memory technology, data will not be lost when the computer is turned off or suddenly or unexpectedly shut down. This research puts forward the design of an antiexercise ECG monitoring system that collects ECG signals for a group that is in the process of exercise. Use mathematical calculation system to classify, and conduct in-depth analysis of the reasons for poor anti-interference ability, and use the original denoising algorithm calculation as the basis to adapt to the dynamic situation in the opposite situation to the normal ECG signal acquisition in the static state. The collection is modified from the original basis. This research proposes an improved scheme for multichannel acquisition. Chips of semiconductor memory devices based on nonvolatile memory are embedded in mobile devices for use. The experimental results in this paper show that the comparison experiment ratio of collecting ECG signals from the sixth, third, fourth, and fifth directions can effectively reduce the mean square error (MSE), and the error of channel 6 is directly from 0.1598. It is down to 0.00143. Several other items showed a significant decrease. It shows that the anti-interference ability of the ECG monitoring system can be further enhanced by increasing the number of collection terminals.

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A Thunderstorm Cloud Point Charge Localization Method Based on CEEMDAN and SG Filtering

2021

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A Survey on Ecg Signal Denoising Using S-Transform and Sg Filtering

Cited by 6 publications

References 7 publications

Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho

Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho

A New Type of ECG Signal Acquisition and Storage Nonvolatile Chip Embedded in Mobile Devices for Sports Monitoring

A Thunderstorm Cloud Point Charge Localization Method Based on CEEMDAN and SG Filtering

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